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StopWordsRemovingEstimator Class

Definition

public sealed class StopWordsRemovingEstimator : Microsoft.ML.Data.TrivialEstimator<Microsoft.ML.Transforms.Text.StopWordsRemovingTransformer>
type StopWordsRemovingEstimator = class
    inherit TrivialEstimator<StopWordsRemovingTransformer>
Public NotInheritable Class StopWordsRemovingEstimator
Inherits TrivialEstimator(Of StopWordsRemovingTransformer)
Inheritance

Remarks

Estimator Characteristics

Does this estimator need to look at the data to train its parameters? No
Input column data type Vector of Text
Output column data type Variable-sized vector of Text
Exportable to ONNX Yes

The resulting StopWordsRemovingTransformer creates a new column, named as specified in the output column name parameter, and fills it with a vector of words containing all of the words in the input column **except the predefined list of stopwords for the specified language. All text comparison made by casting predefined text and text from input column to lower case using casing rules of invariant culture.

Check the See Also section for links to usage examples.

Methods

Fit(IDataView) (Inherited from TrivialEstimator<TTransformer>)
GetOutputSchema(SchemaShape)

Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline.

Extension Methods

AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment)

Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.

WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>)

Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.

Applies to

See also